Bibliographic record
Abstract
Purpose This paper examines the relevance of the wicked problem continuum, particularly the emergence of super wicked challenges for public leadership researchers. Contemporary theorizing on public leadership adequately deals with tame challenges, struggles with wicked problems and remains in the dark with regards to the implications of super wicked problems Design/methodology/approach The wicked problem continuum provides a typology or set of dilemmas running from tame to wicked through to super wicked problems. These different problem types are treated as if they were on a three-zone continuum in which the difficulty of solving or substantially reducing the problem varies from relatively low to very high. Findings We delineate the three-problem contexts in the wicked problem continuum and discuss the ideal type of organization thriving in each zone. We then posit two opposing wicked problem interpretations-taming and wilding- for those interested in public leadership. Taming calls for prudent, results-oriented leaders employing tried and tested practices. Wilding demands leaders who test the status quo by seeking alternatives. Social implications On the global leadership agenda, wilding problems—those calling attention to the super wicked zone—are escalating. Despite this, public leaders' training lacks a framework for making sense of these urgent and publicly contentious super wicked problems. Originality/value Public policy researchers are beginning to direct attention to super wicked problems such as climate change, and pandemics. This work introduces the wicked problem continuum and demonstrates its pertinence for researchers of public leadership.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.061 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".